From cloud to chip: running generative AI at the edge device

Until recently, running Generative AI locally was nearly impossible. Today, thanks to advancements in model optimization, the development of specialized hardware, and the automation of model tuning, deploying generative AI application close to the data source has become a realistic possibility. For engineers, this brings a new set of challenges: how to effectively compress a diffusion model or LLM to fit on a resource-constrained device? How to split a model architecture between local and remote components? How to leverage AutoML for automatically adapting models to specific hardware platforms?

In this article, we’ll present concrete technical and architectural strategies used to implement Generative AI at the edge. You’ll see a comparison of architectures (GAN vs. diffusion vs. LLM), explore examples of lightweight models, and learn how modern approaches to distributed inference are structured. If you’re working on embedded systems, mobile devices, or Internet of Things (IoT) solutions—and you’re interested in practical deployment of artificial intelligence — this content will offer actionable insights and inspiration.

Choosing the right model architecture for edge AI technology

The choice of generative model architecture plays a crucial role when deploying AI on edge devices, where computational resources are heavily limited. Among the most commonly used approaches are GANs (Generative Adversarial Networks), diffusion models, and large language models (LLMs). Each has its strengths and weaknesses in the context of edge computing. GANs are characterized by relatively fast inference and lower memory requirements, making them easier to optimize for edge use—especially in lightweight variants like MobileGAN, which was specifically designed for mobile hardware. Diffusion models, on the other hand, offer superior output quality, particularly for image generation tasks, but are significantly more computationally demanding in terms of both inference time and energy consumption, making them harder to apply without architectural changes. As highlighted by Dongqi Zheng: “Diffusion models have shown remarkable capabilities in generating high-fidelity data across modalities such as images, audio, and video. However, their computational intensity makes deployment on edge devices a significant challenge.” LLMs such as GPT or BERT are increasingly appearing on edge in the form of distilled models (e.g., DistilBERT, TinyGPT), which retain much of the original model’s functionality while dramatically reducing the number of parameters — for example, DistilBERT has 40% fewer parameters than BERT while retaining over 95% of its language understanding capabilities.

Criterion	GAN (Generative Adversarial Networks)	Diffusion Models	LLM (Large Language Models)
Application	Image, video, data generation	Photorealistic image generation	Text generation, language understanding
Edge performance	Medium	Low	Medium to low
Computational complexity	High	Very high	Very high
Compressibility	Possible	Difficult	Possible
Generation time	Fast	Slow	Fast for small models
Lightweight versions	Yes	Rare	Yes
Energy efficiency	Medium	Low	Depends on the model
Edge deployment tools	TensorFlow Lite, Core ML	No common edge implementations	ONNX, TensorRT, GGML

Tab. 1 Comparison of GANs, Diffusion Models, and LLMs in the context of edge AI

In response to hardware constraints, model splitting is gaining traction as an effective strategy. It involves partitioning the model so that part of the computation is executed locally, while the more resource-intensive segments are offloaded to the cloud or to another device within the edge network. For example, initial preprocessing and low-level inference can happen on-device, while higher-level processing runs remotely. Combined with truncated inference—which shortens the depth of computation with minimal impact on output quality—this hybrid architecture enables a balance between performance, output fidelity, and resource efficiency. As a result, it becomes feasible to deploy Generative AI in real-world edge scenarios while maintaining responsiveness, energy efficiency, and privacy.

Leverage model optimization and compression strategies

To enable the efficient deployment of generative models on edge devices, it is essential to apply advanced model optimization and compression techniques. Standard architectures like transformers (e.g., GPT-2, BERT), diffusion models, and GANs often consist of hundreds of millions of parameters and require significant computational power and memory, making them impractical to run directly on resource-constrained devices such as smartphones, cameras, wearables, or IoT sensors. Therefore, reducing model size while maintaining generation quality is a key challenge.

The most commonly used techniques include:

Pruning – removing low-importance or inactive connections in the neural network, reducing the number of operations and memory required for inference.
Quantization – representing weights and activations using lower precision (e.g., INT8 instead of FP32), which significantly speeds up model execution and reduces its footprint. As wrote Jahid Hasan in his book: “Quantization can achieve up to a 68% reduction in model size while maintaining performance within 6% of full precision”.
Knowledge distillation – training a smaller model (student) to mimic the outputs of a larger, more accurate model (teacher), allowing for high-quality predictions with fewer resources.

In practice, specialized frameworks and libraries are used to support these processes, such as TensorRT, ONNX Runtime, TensorFlow Lite, Apple Core ML, TVM, and the Apache Deep Learning Compiler. These tools help convert and optimize models for specific hardware architectures (CPU, GPU, NPU) to achieve maximum efficiency.

Combined with the rapid advancement of dedicated AI hardware, such as Neural Processing Units (NPUs), these techniques enable the deployment of advanced Generative AI directly on edge devices — preserving privacy, reducing latency, and allowing systems to operate independently of the cloud.

Memory and computation: managing AI on the edge infrastructure

Deploying generative AI in edge environments requires much more than just adapting a model to hardware constraints — real-time resource and data processing management is absolutely critical. Even after initial optimization, models may still occupy hundreds of megabytes of memory and generate intensive matrix operations that can overload local processors and power systems. In conditions of limited RAM availability, narrow data bus bandwidth, and energy constraints, every clock cycle and memory access operation matters.

How can we ensure stable and efficient model execution under such strict hardware limitations? That’s why designing edge systems with Generative AI demands precise resource profiling and predictable memory planning — both volatile and persistent. Models must be loaded, executed, and released deterministically, often using manually managed buffers or memory shared across components. It also becomes crucial to minimize peak load spikes, which can lead to device overheating, performance throttling, or operational instability.

Modern approaches also incorporate adaptive workload management based on device context — battery state, temperature, task priority, or network availability. These strategies allow for dynamic scaling of computational intensity or smooth switching between local and remote processing — known as edge-cloud offloading. The key is to maintain continuous operation with minimal impact on latency and output quality, which often requires tight coordination between the AI model, operating system, and hardware layer.

Adversarial threats and model theft on the edge device

Generative models running locally, while reducing the need to transmit data to the cloud, are exposed to a distinct class of threats specific to edge environments. The most critical include:

Model inversion
An attacker, with access to the model and its outputs, attempts to reconstruct training data — such as facial images, document contents, or voice recordings. In systems that learn locally, the risk of leaking sensitive user data increases significantly.
Model extraction
Involves systematically querying the model to reverse-engineer its architecture and weights. This can lead to the theft of intellectual property or confidential information, especially if the model has been fine-tuned on proprietary data.
Adversarial examples
Intentionally crafted inputs designed to mislead the model. In the context of Generative AI, this can result in distorted images, misleading generated text, or erratic behavior in user-facing interfaces.

Effective defense against these attacks requires a multi-layered approach: improving model robustness (e.g., via adversarial training), restricting access to inference APIs, leveraging secure hardware environments (such as secure enclaves), and continuously monitoring for anomalies. For teams deploying AI at the edge, addressing these threats is not just a technical necessity — it’s a cornerstone of building trustworthy and compliant AI systems.

Compact models, big impact: shaping the future of edge computing

One of the most important trends shaping the future of Generative AI on edge devices is the development of specialized computing hardware that significantly boosts local processing capabilities. Neural Processing Units (NPUs) and Tensor Processing Units (TPUs), designed specifically for machine learning tasks, offer high performance with low energy consumption — which is crucial for mobile devices, wearables, and embedded systems. Increasing attention is also being paid to neuromorphic chips, inspired by the structure of the human brain, enabling inference at extremely low power levels.

In parallel, we are seeing the evolution of foundation models — large language, visual, and multimodal models that are pre-trained on massive datasets and then adapted to specific tasks. In the context of edge deployment, these models are increasingly being designed with hardware constraints in mind. Examples include:

TinyLLaMA – a miniaturized version of LLaMA tailored for memory-constrained devices,
MobileBERT – a compact language model optimized for CPU/NPU operation,
Lightweight diffusion models – simplified models for real-time image generation on mobile hardware.

Another critical direction is the growth of AutoML for edge, which allows automatic tuning and optimization of models for specific hardware conditions. Tools include:

Google Edge TPU Compiler,
AWS SageMaker Neo,
Apache TVM – an open-source framework for compiling models across different hardware backends.

These technologies enable engineers to focus on functionality and business value rather than manual parameter tuning, significantly accelerating and simplifying the deployment of Generative AI on the edge. Combined, these advancements make edge AI not only technically feasible but also scalable, efficient, and ready for real-world applications.

You can discover many interesting things about AI in embedded systems from our article:

The Future of Embedded Systems: AI – Driven Innovations

Edge AI use case: combining generative models and modular design in real-time systems

One of the more compelling examples of applying Generative AI in an edge environment was a project carried out by InTechHouse, aimed at streamlining the development of advanced AI-based filters. The client — a company from the medical technology sector — was struggling with low efficiency in developing AI filters used for processing biological signals. In response to these challenges, InTechHouse experts designed a flexible and scalable architecture based on a modular approach to model training and testing. A key aspect of the project was adapting the models to operate in hardware-constrained environments, which opened the door to edge computing deployments — for example, on diagnostic or patient monitoring devices operating in real time and dealing with a continuous amount of data from physiological signals..

The team also implemented tools for automating experiments (AutoML) as well as integrated solutions for version control and team collaboration, significantly accelerating the development cycle. As a result, the client not only achieved higher-quality filters and more accurate results, but also reduced deployment time by over 30%. This case demonstrates how combining modern AI techniques with a well-thought-out edge computing infrastructure can deliver tangible benefits — both technological and organizational. It is a model example of how complex AI systems can be optimized for local operation without compromising performance or precision.

More about this implementation you can read here:

Streamlining AI Filter Development and Improving Team Collaboration

Edge computing and generative AI: how InTechHouse brings AI closer to the data

Generative AI is no longer confined to data centers — it’s rapidly entering the world of edge computing. Models that once required a GPU cluster can now — with the right compression and optimization — run on smartphones, IoT devices, or even microcontrollers equipped with NPUs. This isn’t just a technological milestone; it marks a paradigm shift: data is increasingly being processed where it is generated, not where the cloud happens to be.

If you’re looking for a technology partner who understands both the challenges of generative models and the realities of edge deployment, InTechHouse is ready to support you at every stage of your project — from concept and prototyping to optimization, integration, and scaling. We have hands-on experience delivering AI solutions in production environments, including for industries that demand high reliability, low latency, and full control over their data.

Get in touch with us to see how we can help your team take AI to the next level — closer to the user, closer to the data, and closer to real-world results.

FAQ

Are diffusion models too heavy for edge devices?
In their original form, yes. However, there are techniques to accelerate their performance, such as model truncation, knowledge distillation, or deploying only parts of the architecture locally.

What kind of hardware can run Generative AI locally?
Smartphones with NPUs, SoCs with AI accelerators, single-board computers (e.g., Jetson Nano), and new generations of microcontrollers that support inference.

Is it worth using quantization-aware training when deploying models on the edge?
Yes – QAT (Quantization-Aware Training) provides better results after model quantization than standard post-training conversion, especially when targeting INT8.

What are the differences between DistilBERT and MobileBERT in the context of edge deployment?
DistilBERT is a reduced model obtained through distillation, while MobileBERT was designed from the ground up for mobile inference – its architecture is more optimized for memory and performance.

Generative AI on the Edge Devices: Efficiency Without the Cloud

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